Summary of simulated trial results

Summarises simulation results from the run_trials() function. Uses extract_results() and check_performance(), which may be used directly to extract key trial results without summarising or to calculate performance metrics (with uncertainty measures if desired) and return them in a tidy data.frame.

Usage

# S3 method for trial_results
summary(
  object,
  select_strategy = "control if available",
  select_last_arm = FALSE,
  select_preferences = NULL,
  te_comp = NULL,
  raw_ests = FALSE,
  final_ests = NULL,
  restrict = NULL,
  cores = NULL,
  ...
)

Arguments

object

trial_results object, output from the run_trials() function.

select_strategy

single character string. If a trial was not stopped due to superiority (or had only 1 arm remaining, if select_last_arm is set to TRUE in trial designs with a common control arm; see below), this parameter specifies which arm will be considered selected when calculating trial design performance metrics, as described below; this corresponds to the consequence of an inconclusive trial, i.e., which arm would then be used in practice.
The following options are available and must be written exactly as below (case sensitive, cannot be abbreviated):

"control if available" (default): selects the first control arm for trials with a common control arm if this arm is active at end-of-trial, otherwise no arm will be selected. For trial designs without a common control, no arm will be selected.
"none": selects no arm in trials not ending with superiority.
"control": similar to "control if available", but will throw an error if used for trial designs without a common control arm.
"final control": selects the final control arm regardless of whether the trial was stopped for practical equivalence, futility, or at the maximum sample size; this strategy can only be specified for trial designs with a common control arm.
"control or best": selects the first control arm if still active at end-of-trial, otherwise selects the best remaining arm (defined as the remaining arm with the highest probability of being the best in the last adaptive analysis conducted). Only works for trial designs with a common control arm.
"best": selects the best remaining arm (as described under "control or best").
"list or best": selects the first remaining arm from a specified list (specified using select_preferences, technically a character vector). If none of these arms are are active at end-of-trial, the best remaining arm will be selected (as described above).
"list": as specified above, but if no arms on the provided list remain active at end-of-trial, no arm is selected.

select_last_arm

single logical, defaults to FALSE. If TRUE, the only remaining active arm (the last control) will be selected in trials with a common control arm ending with equivalence or futility, before considering the options specified in select_strategy. Must be FALSE for trial designs without a common control arm.

select_preferences

character vector specifying a number of arms used for selection if one of the "list or best" or "list" options are specified for select_strategy. Can only contain valid arms available in the trial.

te_comp

character string, treatment-effect comparator. Can be either NULL (the default) in which case the first control arm is used for trial designs with a common control arm, or a string naming a single trial arm. Will be used when calculating err_te and sq_err_te (the error and the squared error of the treatment effect comparing the selected arm to the comparator arm, as described below).

raw_ests

single logical. If FALSE (default), the posterior estimates (post_ests or post_ests_all, see setup_trial() and run_trial()) will be used to calculate err and sq_err (the error and the squared error of the estimated compared to the specified effect in the selected arm) and err_te and sq_err_te (the error and the squared error of the treatment effect comparing the selected arm to the comparator arm, as described for te_comp and below). If TRUE, the raw estimates (raw_ests or raw_ests_all, see setup_trial() and run_trial()) will be used instead of the posterior estimates.

final_ests

single logical. If TRUE (recommended) the final estimates calculated using outcome data from all patients randomised when trials are stopped are used (post_ests_all or raw_ests_all, see setup_trial() and run_trial()); if FALSE, the estimates calculated for each arm when an arm is stopped (or at the last adaptive analysis if not before) using data from patients having reach followed up at this time point and not all patients randomised are used (post_ests or raw_ests, see setup_trial() and run_trial()). If NULL (the default), this argument will be set to FALSE if outcome data are available immediate after randomisation for all patients (for backwards compatibility, as final posterior estimates may vary slightly in this situation, even if using the same data); otherwise it will be said to TRUE. See setup_trial() for more details on how these estimates are calculated.

restrict

single character string or NULL. If NULL (default), results are summarised for all simulations; if "superior", results are summarised for simulations ending with superiority only; if "selected", results are summarised for simulations ending with a selected arm only (according to the specified arm selection strategy for simulations not ending with superiority). Some summary measures (e.g., prob_conclusive) have substantially different interpretations if restricted, but are calculated nonetheless.

cores

NULL or single integer. If NULL, a default value set by setup_cluster() will be used to control whether extractions of simulation results are done in parallel on a default cluster or sequentially in the main process; if a value has not been specified by setup_cluster(), cores will then be set to the value stored in the global "mc.cores" option (if previously set by options(mc.cores = <number of cores>), and 1 if that option has not been specified.
If cores = 1, computations will be run sequentially in the primary process, and if cores > 1, a new parallel cluster will be setup using the parallel library and removed once the function completes. See setup_cluster() for details.

...

additional arguments, not used.

Value

A "trial_results_summary" object containing the following values:

n_rep: the number of simulations.
n_summarised: as described in check_performance().
highest_is_best: as specified in setup_trial().
elapsed_time: the total simulation time.
size_mean, size_sd, size_median, size_p25, size_p75, size_p0, size_p100, sum_ys_mean, sum_ys_sd, sum_ys_median, sum_ys_p25, sum_ys_p75, sum_ys_p0, sum_ys_p100, ratio_ys_mean, ratio_ys_sd, ratio_ys_median, ratio_ys_p25, ratio_ys_p75, ratio_ys_p0, ratio_ys_p100, prob_conclusive, prob_superior, prob_equivalence, prob_futility, prob_max, prob_select_* (with * being either "arm_<name> for all arm names or none), rmse, rmse_te, mae, mae_te, and idp: performance metrics as described in check_performance(). Note that all sum_ys_ and ratio_ys_ measures use outcome data from all randomised patients, regardless of whether they had outcome data available at the last analysis or not, as described in extract_results().
select_strategy, select_last_arm, select_preferences, te_comp, raw_ests, final_ests, restrict: as specified above.
control: the control arm specified by setup_trial(), setup_trial_binom() or setup_trial_norm(); NULL if no control.
equivalence_assessed, futility_assessed: single logicals, specifies whether the trial design specification includes assessments of equivalence and/or futility.
base_seed: as specified in run_trials().
cri_width, n_draws, robust, description, add_info: as specified in setup_trial(), setup_trial_binom() or setup_trial_norm().

Examples

# Setup a trial specification
binom_trial <- setup_trial_binom(arms = c("A", "B", "C", "D"),
                                 control = "A",
                                 true_ys = c(0.20, 0.18, 0.22, 0.24),
                                 data_looks = 1:20 * 100)

# Run 10 simulations with a specified random base seed
res <- run_trials(binom_trial, n_rep = 10, base_seed = 12345)

# Summarise simulations - select the control arm if available in trials not
# ending with a superiority decision
res_sum <- summary(res, select_strategy = "control")

# Print summary
print(res_sum, digits = 1)
#> Multiple simulation results: generic binomially distributed outcome trial
#> * Undesirable outcome
#> * Number of simulations: 10
#> * Number of simulations summarised: 10 (all trials)
#> * Common control arm: A
#> * Selection strategy: first control if available (otherwise no selection)
#> * Treatment effect compared to: no comparison
#> 
#> Performance metrics (using posterior estimates from last adaptive analysis):
#> * Sample sizes: mean 1840.0 (SD: 506.0) | median 2000.0 (IQR: 2000.0 to 2000.0) [range: 400.0 to 2000.0]
#> * Total summarised outcomes: mean 369.9 (SD: 105.4) | median 390.0 (IQR: 376.5 to 408.5) [range: 84.0 to 466.0]
#> * Total summarised outcome rates: mean 0.202 (SD: 0.016) | median 0.196 (IQR: 0.194 to 0.209) [range: 0.180 to 0.233]
#> * Conclusive: 10.0%
#> * Superiority: 10.0%
#> * Equivalence: 0.0% [not assessed]
#> * Futility: 0.0% [not assessed]
#> * Inconclusive at max sample size: 90.0%
#> * Selection probabilities: A: 80.0% | B: 10.0% | C: 0.0% | D: 0.0% | None: 10.0%
#> * RMSE / MAE: 0.02061 / 0.01915
#> * RMSE / MAE treatment effect: 0.18206 / 0.18206
#> * Ideal design percentage: 70.4%
#> 
#> Simulation details:
#> * Simulation time: 0.695 secs
#> * Base random seed: 12345
#> * Credible interval width: 95%
#> * Number of posterior draws: 5000
#> * Estimation method: posterior medians with MAD-SDs

Summary of simulated trial results

Usage

Arguments

Value

See also

Examples